IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
SybilGuard: defending against sybil attacks via social networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges
IEEE Internet Computing
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
All your contacts are belong to us: automated identity theft attacks on social networks
Proceedings of the 18th international conference on World wide web
Sybil-resilient online content voting
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Friend-in-the-Middle Attacks: Exploiting Social Networking Sites for Spam
IEEE Internet Computing
Uncovering social network sybils in the wild
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Understanding and combating link farming in the twitter social network
Proceedings of the 21st international conference on World Wide Web
Proceedings of the 21st international conference on World Wide Web
Detecting and Validating Sybil Groups in the Wild
ICDCSW '12 Proceedings of the 2012 32nd International Conference on Distributed Computing Systems Workshops
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Recently Online Social Networks (OSNs) are enjoying a continuous boom, while suffering from omnifarious malicious attacks. Cloning attack is one of the attack patterns towards online social networks, where typically the attacker disguises fake accounts as real users by thieving and copying their profiles, and sends friend requests to the friends of the cloned victim. It is difficult for ordinary users to detect these fake identities because of the identical names and similar profile information. In this paper, we raise two possible improvements, namely snowball sampling and iteration attack, to the regular attack pattern upgrading its efficiency and power, so that the attackers can more easily engage into the community. An experiment has been conducted on Renren, the largest OSN in China, to fully compare and substantiate the effectiveness of the enhanced strategy with traditional attacks and different levels of cloning attacks. Then we discuss approaches to detect cloning attacks and put forward a detector named CloneSpotter, which can be deployed into OSN servers. The detector takes advantage of the detailed login IP records and provides solid evidence of locations, in order to judge whether the suspicious accounts are manipulated by real users or attackers. Besides, we discuss a content-based approach to protect users from cloning attacks, which can be easily implemented into distributed clients. Our contribution lies in two aspects. First, we improve a threatening attack pattern towards OSNs, and test its effectiveness in real systems. Second, we provide an effective defense method to detect cloning attacks, which is real-time and lightweight. By deploying the detectors, OSN systems can assist users to accurately distinguish cloning accounts, and safeguard their privacy.